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Prediction of Micro Plasma impacts in organic Vegetables Using Deep Learning


Affiliations
1 Department of Computer Science and Engineering, St. Mother Theresa Engineering College, India
2 Department of Electrical and Electronics Engineering, PSNA College of Engineering and Technology, India
3 Department of Electronics and Communication Engineering, P.S.V College of Engineering and Technology, India
4 Department of Electronics and Communication Engineering, SSM College of Engineering, India
     

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The potential of micro plasma impacts in the growth of organic vegetables has been very difficult to predict. With the advent of deep learning methods, scientists are now able to develop predictive models that can accurately assess the effects of these impacts. Deep learning algorithms can be used to analyze the various environmental factors that influence the growth of organic vegetables, including temperature, humidity, sunlight, and soil type. With these inputs, the deep learning algorithms can learn complex relationships between these elements and the output of the growth of organic vegetables. By considering the interactions between environment and micro plasma impacts, the deep learning algorithms can make accurate predictions regarding the effects of these impacts on organic vegetable yields. The algorithm can be optimized to accurately predict the effect of these impacts in the future, allowing farmers to better plan for their crops. In addition, the deep learning algorithms can be used to analyze the effects of various factors on micro plasma impacts in organic vegetables. For example, the algorithm can analyze the effects of different combinations of fertilizer, water, and chemical inputs on the micro plasma impacts, allowing farmers to find the optimal crop growth conditions more quickly. The use of deep learning to predict the effects of micro plasma impacts on organic vegetable growth has the potential to improve crop yields, leading to more efficient agriculture practices.

Keywords

Micro Plasma, Organic, Vegetables, Deep Learning, Humidity, Temperature, Sunlight.
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  • R. Manikandan, S.S. Priscila and M. Ramkumar, “Sequential Pattern Mining on Chemical Bonding Database in the Bioinformatics Field”, Proceedings of International Conference on AIP Publishing, pp. 1-13, 2022.

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  • P. Saxena and A. Sharma, “The Deep DNA Machine Learning Model to Classify the Tumor Genome of Patients with Tumor Sequencing”, International Journal of Health Sciences, Vol. 6, No. 5, pp. 9364-9375, 2022.

  • G. Ramesh and K. Rajkumar, “The Smart Construction for Image Preprocessing of Mobile Robotic Systems using Neuro Fuzzy Logical System Approach”, NeuroQuantology, Vol. 20, No. 10, pp. 6354-6367, 2022.

  • T.R. Sivapriya and V. Saravanan, “Automatic Brain MRI Mining using Support Vector Machine and Decision Tree”,CiiT International Journal of Artificial Intelligent Systems and Machine Learning, Vol. 3, No. 2, pp. 109-116, 2011.

  • A. Hafeez and F. Rehman, “Optimization on Cleaner Intensification of Ozone Production using Artificial Neural Network and Response Surface Methodology: Parametric and Comparative Study”, Journal of Cleaner Production, Vol. 252, pp. 119833-119843, 2020.

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  • M.J. Rist, B. Merz and B. Watzl, “Metabolite Patterns Predicting Sex and Age in Participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) Study”, PloS One, Vol. 12, No. 8, pp. 1-13, 2017.


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  • Prediction of Micro Plasma impacts in organic Vegetables Using Deep Learning

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Authors

J. Jasper Gnana Chandran
Department of Computer Science and Engineering, St. Mother Theresa Engineering College, India
L. Sahaya Senthamil
Department of Electrical and Electronics Engineering, PSNA College of Engineering and Technology, India
C. Thamilarasi
Department of Electronics and Communication Engineering, P.S.V College of Engineering and Technology, India
J. Jaganpradeep
Department of Electronics and Communication Engineering, SSM College of Engineering, India

Abstract


The potential of micro plasma impacts in the growth of organic vegetables has been very difficult to predict. With the advent of deep learning methods, scientists are now able to develop predictive models that can accurately assess the effects of these impacts. Deep learning algorithms can be used to analyze the various environmental factors that influence the growth of organic vegetables, including temperature, humidity, sunlight, and soil type. With these inputs, the deep learning algorithms can learn complex relationships between these elements and the output of the growth of organic vegetables. By considering the interactions between environment and micro plasma impacts, the deep learning algorithms can make accurate predictions regarding the effects of these impacts on organic vegetable yields. The algorithm can be optimized to accurately predict the effect of these impacts in the future, allowing farmers to better plan for their crops. In addition, the deep learning algorithms can be used to analyze the effects of various factors on micro plasma impacts in organic vegetables. For example, the algorithm can analyze the effects of different combinations of fertilizer, water, and chemical inputs on the micro plasma impacts, allowing farmers to find the optimal crop growth conditions more quickly. The use of deep learning to predict the effects of micro plasma impacts on organic vegetable growth has the potential to improve crop yields, leading to more efficient agriculture practices.

Keywords


Micro Plasma, Organic, Vegetables, Deep Learning, Humidity, Temperature, Sunlight.

References